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Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth

Tai-Shen Chen¹, Toru Aoike¹, Masanori Yamasaki²

¹Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Japan.

Frontiers in Genetics

|January 4, 2021

Summary

Accurate prediction of rice heading date is crucial for cultivation and breeding. An integrated approach combining machine learning and crop growth models accurately predicts heading date for new rice varieties in diverse environments.

Keywords:

Markov chain Monte-Carlo bayesian inference crop growth model differential evolution adaptive metropolis machine learning

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Area of Science:

Agricultural Science
Plant Breeding
Computational Biology

Background:

Days to heading (DTH) is a complex trait influenced by genetics and environment.
Crop growth models (CGMs) predict plant development but require extensive calibration data.
Genotype-specific parameters in CGMs are typically estimated individually for each cultivar.

Purpose of the Study:

To develop an integrated approach for predicting rice heading date.
To link genotype marker data with CGM parameters using machine learning.
To enable accurate heading date prediction for new genotypes in novel environments.

Main Methods:

Implemented a Bayesian approach with differential evolution adaptive metropolis algorithm for parameter estimation.
Utilized a large dataset of rice heading dates and environmental variables (112 cultivars, 7 locations, 14 years).
Integrated genotype marker data with CGM parameters via a machine learning model.

Main Results:

The proposed integrated approach outperformed both CGMs and standalone machine learning models for predicting DTH in untested genotypes and environments.
Extreme learning machine showed superior predictive accuracy compared to CGMs for tested genotypes/locations.
High correlation (r ≈ 0.8) observed between predicted and observed DTH distribution percentiles in segregation populations.

Conclusions:

The integration of machine learning and CGMs offers a powerful tool for predicting rice heading dates.
This approach enhances the prediction accuracy for new rice cultivars in untested environments.
Facilitates improved decision-making in rice cultivation management and breeding programs.